On‐Chip Neural Induction Boosts Neural Stem Cell Commitment: Toward a Pipeline for iPSC‐Based Therapies

Abstract The clinical translation of induced pluripotent stem cells (iPSCs) holds great potential for personalized therapeutics. However, one of the main obstacles is that the current workflow to generate iPSCs is expensive, time‐consuming, and requires standardization. A simplified and cost‐effective microfluidic approach is presented for reprogramming fibroblasts into iPSCs and their subsequent differentiation into neural stem cells (NSCs). This method exploits microphysiological technology, providing a 100‐fold reduction in reagents for reprogramming and a ninefold reduction in number of input cells. The iPSCs generated from microfluidic reprogramming of fibroblasts show upregulation of pluripotency markers and downregulation of fibroblast markers, on par with those reprogrammed in standard well‐conditions. The NSCs differentiated in microfluidic chips show upregulation of neuroectodermal markers (ZIC1, PAX6, SOX1), highlighting their propensity for nervous system development. Cells obtained on conventional well plates and microfluidic chips are compared for reprogramming and neural induction by bulk RNA sequencing. Pathway enrichment analysis of NSCs from chip showed neural stem cell development enrichment and boosted commitment to neural stem cell lineage in initial phases of neural induction, attributed to a confined environment in a microfluidic chip. This method provides a cost‐effective pipeline to reprogram and differentiate iPSCs for therapeutics compliant with current good manufacturing practices.

, Table S10 and Table S11.The error bars signify the standard deviation, the centerline signifies the median value, the square signifies the mean, and the box plots correspond to the 25 and 75 percentiles.

Figure S1 :
Figure S1: Characterization of iPSCs obtained by reprogramming fibroblasts in the microfluidic format: Number of colonies obtained per 10 mm 2 surface area in the microfluidic devices, with each point on the plot representing data from one image frame.

Figure S2 :
Figure S2: Pluripotency expression in iPSCs generated on chips or in wells.Flow cytometric scatter plots showing pluripotency expression in iPSCs reprogrammed in well or chip, after single cell expansion.KTH-04: well p 19, chip p 20. KTH-05: well p 14, chip p14.KTH-06: well p 14, chip p14.FMO controls were used as negative controls to set the gates on the respective iPSC line co-stained for OCT4, SOX2, SSEA4 and TRA-1-60.

Figure S3 :
Figure S3: Gating strategies for flow cytometry analysis of pluripotency co-expression in Figure 2c.Flow cytometric scatter plots showing co-expression of intracellular markers OCT4 and SOX2 and extracellular markers SSEA4 and TRA-1-60 in iPSCs reprogrammed in well or chip, respectively.KTH-04: well p 19, chip p 20. KTH-05: well p 14, chip p14.KTH-06: well p 14, chip p14.Cell line-specific FMO controls were used as negative controls to set the gates.

Figure S4 :
Figure S4: Pluripotency expression in clonal iPSC lines.Flow cytometric scatter plots showing pluripotency expression in iPSC lines obtained from the iPS Core at Karolinska Institute: Control 7-II p 18, Control 10-V p 33, Control 14-II p 38. FMO controls were used as negative controls to set the gates on the respective iPSC line co-stained for OCT4, SOX2, SSEA4, and TRA-1-60.

Figure S5 :
Figure S5: An overview of the intensity of expression for OCT4, SOX2, SSEA4, and TRA-1-60 in iPSCs reprogrammed in well or chip after single cell expansion.Quantitative expression data are shown in flow cytometric scatter plots in Figure S4.This used the following cell lines KTH-04: well p 19, chip p 20. KTH-05: well p 14, chip p14.KTH-06: well p 14, chip p 14.

Figure S6 :
Figure S6: PCA of iPSCs obtained by reprogramming in a conventional well plate and microfluidic platforms.iPSCs obtained from both conditions usually cluster close to each other, highlighting no substantial difference between the cells obtained.

Figure S7 :
Figure S7: Statistical analysis of the protein expression data obtained by qPCR on neural differentiated cells on both well and chip culture formats.The p-values were determined using the Linear Mixed Model on OriginPro 2023b.There were three biological replicates analyzed for each data point, and each point represents one technical replicate.The biological replicates are listed in TableS9, TableS10and TableS11.The error bars signify the standard

Figure S8 :
Figure S8: Hierarchical clustering of NSC for the time point "generation" for the 300 most variable genes.NSCs generated on chips (yellow) clustered together; the same pattern was evident for NSCs generated in wells (blue).The deviation of C5 is also evident here, where the chip and well conditions are associated more with each other than with the chip and well conditions of C9 and KTH-01.The figure inset provides a better view of the hierarchical clustering observed.

Figure S9 :
Figure S9: Hierarchical clustering of NSC for the time point "maintenance" for the most variable genes.NSCs generated on chips (yellow) clustered together; the same pattern was evident for NSCs generated in wells (blue).The deviation of C5 is also evident here, where the chip and well conditions are associated more with each other than with the chip and well conditions of C9 and KTH-01.